Layout Summary
include product application date (if applicable) Note: Need to clean up IO file organization in the ‘Report’ folder
| Parameter | Value |
|---|---|
| Crop | Corn |
| Cultivar | Beck 6365AM |
| Plant Date | 2024-04-18 |
| Seed Rate | 31000 |
| Application Date | 2025-03-23 |
| Study Area | 1.15 | 50123.53 | 905.34 | 55.36 |
| Plot Size (Avg) | 0.02 | 817.29 | 151.04 | 5.41 |
Experimental Design: Randomized Complete Block Factorial.
| Type | Count |
|---|---|
| Plots | 48 |
| Replications | 4 |
| Treatments | 12 |
NOTE: Need to integrate a new and more evolved data set representing realistic treatments and then actually use the document to tell the story.
| Treatment | Factor A | Factor B |
|---|---|---|
| A | Product A | Time 1 |
| B | Product A | Time 2 |
| C | Product A | Time 3 |
| D | Product A | Time 4 |
| E | Product B | Time 1 |
| F | Product B | Time 2 |
| G | Product B | Time 3 |
| H | Product B | Time 4 |
| I | Product C | Time 1 |
| J | Product C | Time 2 |
| K | Product C | Time 4 |
| K | Product C | Time 3 |
Highly integrated imaging system
Imaging system has one 20MP RGB camera and four 5MP multispectral cameras (green, red, red edge, and near infrared), enabling applications such as high-precision aerial surveying, crop growth monitoring, and natural resource surveys.
5MP multispectral camera
RGB Camera Features
Sunlight sensor
The built-in sunlight sensor captures solar radiation and records it in an image file, allowing light compensation of the image data during 2D reconstruction. This results in more accurate NDVI results as well as increased accuracy and consistency of the acquired data over time.
RTK module
Precise images that capture every pixel. Mavic 3M with RTK module for centimeter-level positioning. The flight controller, camera, and RTK module synchronize within microseconds to accurately capture the location of each camera’s imaging center. This allows Mavic 3M to perform high-precision aerial surveys without using ground control points.
| Parameter | Value |
|---|---|
| Image Count | 229 |
| Height Above Ground Level (m) | 76.2 |
| Ground Sampling Distance (m) | 0.05 |
| Focal Length (mm) | 0.672 |
| Width x Height (px) | 2048 x 1536 |
Make table subsections for internal v external parameters. Need to include IFOV?
More specific flight parameter information on the ODM Quality Report.
| Date | Start | End | Duration |
|---|---|---|---|
| 06/23/21 | 12:43:40 | 12:53:33 | 9 min, 53 sec |
Ground Control Points (GCPs) are crucial for agriculture drone mapping, especially for research purposes. They ensure the precise georeferencing of aerial images, correcting any distortions and aligning the maps with real-world coordinates. By using GCPs, researchers can obtain reliable and actionable data, enabling them to make informed decisions and improve agricultural practices.
| id | latitude | longitude |
|---|---|---|
| 1 | 40.31178 | -85.14850 |
| 2 | 40.31142 | -85.14803 |
| 3 | 40.31178 | -85.14791 |
| 4 | 40.31142 | -85.14841 |
| 5 | 40.31187 | -85.14820 |
Cloud Descriptions
The formula for calculating Growing Degree Days (GDD) is as follows:
\[ \text{GDD} = \frac{T_\text{max} + T_\text{min}}{2} - T_\text{base} \]
where:
\[ T_\text{max} \text{ is the daily maximum temperature,} \]
\[ T_\text{min} \text{ is the daily minimum temperature,} \]
\[ T_\text{base} \text{ is the base temperature below which growth is negligible.} \]
Requirements:
Integration of temperature data derived from the most local weather station.
## [1] "Wind speed, temperature, cloud cover & sun angle during the time imagery was collected. Would like to find a way to get weather data dynamically by using location and aquisition time. This should be considered across multiple flight times - one report or a summary report?"
## $example
## # A tibble: 5 × 5
## id name latitude longitude distance
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 USC00129427 WEST LAFAYETTE PURDUE 40.4 -86.9 0.867
## 2 US1INTP0005 WEST LAFAYETTE 1.6 S 40.4 -86.9 0.938
## 3 USC00129435 W LAFAYETTE SEW PLT 40.4 -86.9 1.51
## 4 US1INTP0074 WEST LAFAYETTE 0.9 SE 40.4 -86.9 2.00
## 5 USW00014835 LAFAYETTE PURDUE UNIV AP 40.4 -86.9 2.57
## # A tibble: 1 × 1
## id
## <chr>
## 1 USC00129427
## # A tibble: 0 × 2
## # ℹ 2 variables: id <chr>, date <date>
## date tmin tmax mean_temp gdd
## 1 2025-02-01 5 15 10 0
## 2 2025-02-02 7 17 12 2
## 3 2025-02-03 8 18 13 3
## 4 2025-02-04 6 16 11 1
## 5 2025-02-05 9 19 14 4
## 6 2025-02-06 10 20 15 5
## 7 2025-02-07 11 21 16 6
Simplified overview of the processing steps
Specify the software and tools used for processing the UAV imagery.
In our small plot research, we use WebODM to process multispectral imagery captured by UAVs. This ensures we can accurately compare treatments and gather valuable insights into crop performance. Here are the key steps involved:
Further information available on the ODM Quality Report.
The Normalized Difference Vegetation Index (NDVI) is a measure of vegetation health and density. For the Mavic 3M, the specific wavelengths for the bands are:
RED band: 650 nm
NIR band: 860 nm
The NDVI formula with these specific wavelengths is:
\[ \text{NDVI} = \frac{(\text{NIR}_{860} - \text{RED}_{650})}{(\text{NIR}_{860} + \text{RED}_{650})} \]
Where:
NIR\(_{860}\) represents the near-infrared light (860 nm) reflected by vegetation.
RED\(_{650}\) represents the red light (650 nm) reflected by vegetation.
The Normalized Difference Red Edge Index (NDRE) is a measure of vegetation health and density, similar to NDVI, but it uses the red-edge band instead of the red band. It is calculated using the formula:
\[ \text{NDRE} = \frac{(\text{NIR} - \text{RE})}{(\text{NIR} + \text{RE})} \]
Where:
NIR represents the near-infrared light reflected by vegetation.
RE represents the red-edge light reflected by vegetation.
The Visible Atmospherically Resistant Index (VARI) is a measure of vegetation health that is less sensitive to atmospheric effects. It is calculated using the formula:
\[ \text{VARI} = \frac{(\text{GREEN} - \text{RED})}{(\text{GREEN} + \text{RED} - \text{BLUE})} \]
Where:
GREEN represents the green light reflected by vegetation.
RED represents the red light reflected by vegetation.
BLUE represents the blue light reflected by vegetation.
Note: Probably should consider moving the statistic summary to this section? Should also consider bringing the GDD to this section
We use ANOVA (Analysis of Variance) and Tukey’s HSD (Honestly Significant Difference) test to analyze remote sensing vegetative reflectance data collected from agricultural research plots. ANOVA helps to test if there are significant differences in the mean NDVI values between different treatment groups, and Tukey’s HSD test identifies which specific groups differ.
ANOVA helps to test if there are significant differences in the mean vegetative reflectance values between different treatment groups. The formula for ANOVA is:
\[ F = \frac{\text{Between-group variability (Mean Square Between)}}{\text{Within-group variability (Mean Square Within)}} \]
where:
\[ \text{Mean Square Between} = \frac{\text{Sum of Squares Between}}{\text{Degrees of Freedom Between}} \]
\[ \text{Mean Square Within} = \frac{\text{Sum of Squares Within}}{\text{Degrees of Freedom Within}} \]
If ANOVA indicates significant differences, we use Tukey’s HSD test to identify which specific groups differ. The formula for Tukey’s HSD is:
\[ \text{HSD} = q_{\alpha} \sqrt{\frac{\text{Mean Square Within}}{n}} \]
where \(q_{\alpha}\) is the studentized range statistic and \(n\) is the sample size of each group.
ANOVA Results: Examine the ANOVA results to determine if there are significant differences between treatment groups based on the F-statistic and p-value.
Tukey’s HSD Results: Use Tukey’s HSD test results to identify which specific treatment groups have significantly different NDVI values.
Inference across multiple NDVI measures
| Treatment | Mean | Median | Min | Max | N | NDVI_M_1 | groups |
|---|---|---|---|---|---|---|---|
| A | 0.5845913 | 0.5845913 | 0.5845913 | 0.5845913 | 4 | 0.5845913 | a |
| B | 0.4270198 | 0.4270198 | 0.4270198 | 0.4270198 | 4 | 0.4270198 | f |
| C | 0.5260286 | 0.5260286 | 0.5260286 | 0.5260286 | 4 | 0.5260286 | b |
| D | 0.3550845 | 0.3550845 | 0.3550845 | 0.3550845 | 4 | 0.3550845 | g |
| E | 0.4939980 | 0.4939980 | 0.4939980 | 0.4939980 | 4 | 0.4939980 | d |
| F | 0.5028109 | 0.5028109 | 0.5028109 | 0.5028109 | 4 | 0.5028109 | c |
| G | 0.3514918 | 0.3514918 | 0.3514918 | 0.3514918 | 4 | 0.3514918 | g |
| H | 0.3104568 | 0.3104568 | 0.3104568 | 0.3104568 | 4 | 0.3104568 | h |
| I | 0.5791387 | 0.5791387 | 0.5791387 | 0.5791387 | 4 | 0.5791387 | a |
| J | 0.4222403 | 0.4222403 | 0.4222403 | 0.4222403 | 4 | 0.4222403 | f |
| K | 0.4743898 | 0.4707931 | 0.4707931 | 0.4923733 | 4 | 0.4761882 | e |
| L | 0.4923733 | 0.4923733 | 0.4923733 | 0.4923733 | 4 | 0.4923733 | d |
| Treatment | Mean | Median | Min | Max | N | NDVI_M_2 | groups |
|---|---|---|---|---|---|---|---|
| A | 0.7427292 | 0.7427292 | 0.7427292 | 0.7427292 | 4 | 0.7427292 | a |
| B | 0.5059123 | 0.5059123 | 0.5059123 | 0.5059123 | 4 | 0.5059123 | f |
| C | 0.5098803 | 0.5098803 | 0.5098803 | 0.5098803 | 4 | 0.5098803 | ef |
| D | 0.4294196 | 0.4294196 | 0.4294196 | 0.4294196 | 4 | 0.4294196 | g |
| E | 0.5488135 | 0.5488135 | 0.5488135 | 0.5488135 | 4 | 0.5488135 | de |
| F | 0.6144698 | 0.6144698 | 0.6144698 | 0.6144698 | 4 | 0.6144698 | bc |
| G | 0.6222045 | 0.6222045 | 0.6222045 | 0.6222045 | 4 | 0.6222045 | b |
| H | 0.5294373 | 0.5294373 | 0.5294373 | 0.5294373 | 4 | 0.5294373 | ef |
| I | 0.5779646 | 0.5779646 | 0.5779646 | 0.5779646 | 4 | 0.5779646 | cd |
| J | 0.5404358 | 0.5404358 | 0.5404358 | 0.5404358 | 4 | 0.5404358 | def |
| K | 0.6194858 | 0.6385688 | 0.5240707 | 0.6385688 | 4 | 0.6099443 | bc |
| L | 0.5240707 | 0.5240707 | 0.5240707 | 0.5240707 | 4 | 0.5240707 | ef |
| Treatment | Mean | Median | Min | Max | N | NDVI_M_3 | groups |
|---|---|---|---|---|---|---|---|
| A | 0.6649717 | 0.6649717 | 0.6649717 | 0.6649717 | 4 | 0.6649717 | d |
| B | 0.8124048 | 0.8124048 | 0.8124048 | 0.8124048 | 4 | 0.8124048 | a |
| C | 0.6751085 | 0.6751085 | 0.6751085 | 0.6751085 | 4 | 0.6751085 | d |
| D | 0.6969188 | 0.6969188 | 0.6969188 | 0.6969188 | 4 | 0.6969188 | cd |
| E | 0.4772124 | 0.4772124 | 0.4772124 | 0.4772124 | 4 | 0.4772124 | e |
| F | 0.6599751 | 0.6599751 | 0.6599751 | 0.6599751 | 4 | 0.6599751 | d |
| G | 0.7379817 | 0.7379817 | 0.7379817 | 0.7379817 | 4 | 0.7379817 | bc |
| H | 0.7389898 | 0.7389898 | 0.7389898 | 0.7389898 | 4 | 0.7389898 | bc |
| I | 0.7045758 | 0.7045758 | 0.7045758 | 0.7045758 | 4 | 0.7045758 | cd |
| J | 0.4961169 | 0.4961169 | 0.4961169 | 0.4961169 | 4 | 0.4961169 | e |
| K | 0.8008585 | 0.8287327 | 0.6614873 | 0.8287327 | 4 | 0.7869214 | ab |
| L | 0.6614873 | 0.6614873 | 0.6614873 | 0.6614873 | 4 | 0.6614873 | d |
| Treatment | Mean | Median | Min | Max | N | NDVI_M_4 | groups |
|---|---|---|---|---|---|---|---|
| A | 0.8481187 | 0.8481187 | 0.8481187 | 0.8481187 | 4 | 0.8481187 | c |
| B | 0.7322143 | 0.7322143 | 0.7322143 | 0.7322143 | 4 | 0.7322143 | e |
| C | 0.8908149 | 0.8908149 | 0.8908149 | 0.8908149 | 4 | 0.8908149 | b |
| D | 0.7555652 | 0.7555652 | 0.7555652 | 0.7555652 | 4 | 0.7555652 | d |
| E | 0.6943518 | 0.6943518 | 0.6943518 | 0.6943518 | 4 | 0.6943518 | f |
| F | 0.8421285 | 0.8421285 | 0.8421285 | 0.8421285 | 4 | 0.8421285 | c |
| G | 0.8767229 | 0.8767229 | 0.8767229 | 0.8767229 | 4 | 0.8767229 | b |
| H | 0.6039105 | 0.6039105 | 0.6039105 | 0.6039105 | 4 | 0.6039105 | g |
| I | 0.9174953 | 0.9174953 | 0.9174953 | 0.9174953 | 4 | 0.9174953 | a |
| J | 0.4638537 | 0.4638537 | 0.4638537 | 0.4638537 | 4 | 0.4638537 | h |
| K | 0.8300481 | 0.8210227 | 0.8210227 | 0.8751748 | 4 | 0.8345607 | c |
| L | 0.8751748 | 0.8751748 | 0.8751748 | 0.8751748 | 4 | 0.8751748 | b |
Inference across multiple NDVI measures
Notes: - Could include maps of all 6 timings, and then compute
statistics on these values? Should they be interactive? TMI for
interactivity?
Could it be something of a GIF with changing figure? Should have only
one legend to represent all of the figures - Need to display multiple VI
- for example NDVI and NDRE
– Need to standardize the legend labels for all of the different growth stages.
Latin Square https://math.montana.edu/jobo/st541/sec3c.pdf?form=MG0AV3
Thank you for your support!
Richard M. Smith, CCA, Ph.D